'''
参数配置文件设置
'''
cuda=True
input_img_size=[608,608]            # 输入的shape大小，一定要是32的倍数，320,416,


#voc_annotation.py参数
layout_path="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/private个人数据集/目标检测数据集/dataset_all/Layout"
where_to_save_txt_path="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/python项目/目标识别/mobilenet-yolov4-pytorch(副本)/split_dataSets"

#get_map.py参数
test_txt_path="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/我的数据集/目标检测数据集/dataset_all/Layout/test.txt"
all_annotation_path="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/private个人数据集/目标检测数据集/dataset_all/Annotations"
all_image_path="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/private个人数据集/目标检测数据集/dataset_all/JPEGImages"
map_out_path="map_out"

#yolo.py参数
test_model_path="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/权重/G-YOLOV4/checkpoint_B0/416/checkpoint_233.pth"

#================================================训练阶段train.py参数================================================
backbone="preffinet_B0"                     #mobilenetv1,mobilenetv2,mobilenetv3,ghostnet,densenet121,densenet169,densenet201,geffinetv2_b0
train_batch_size,val_batch_size = 4,8
continue_train,use_pretrain = True,False           #是否继续训练，是否加载backbone的权重
total_epoch = 300                                   #训练总的epoch
start_epoch = 0                                     #默认开始epoch
lr = 0.001                                          #初始学习率
anchors_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]    # anchors_mask用于帮助代码找到对应的先验框，一般不修改。
freeze = False                                       #是否冻结,训练分为两个阶段，分别是冻结阶段和解冻阶段。
label_smoothing = 0
mosaic = False
lr_descend_method = "CosineAnnealingWarmRestarts"      #学习率下降方法

train_annotation_path="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/private个人数据集/BDD10K/labels/train.txt"
val_annotation_path="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/private个人数据集/BDD10K/labels/val.txt"
classes_path="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/private个人数据集/BDD10K/labels/bdd10k_class.txt"
anchors_path="model_data/yolo_anchors.txt"
train_infor="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/python项目/目标识别/mobilenet-yolov4-pytorch(副本)/logs/infor.txt"
checkpoint_path="/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/权重/PR-YOLOV4/BDD10K/B0_608"                   #模型权重保存路径
ckpt_resume_path = '/media/jiji/3b75ab20-92d3-4be6-81c2-4e1798e2fe16/权重/PR-YOLOV4/BDD10K/B0_608/last_ckpt.pth'           #模型恢复路径


